用大型语言模型生成临床数据的两个方向:数据到标签和标签到数据
Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|January 12, 2024
概括
大型语言模型 (LLM) 可以增强用于阿尔茨海默病 (AD) 检测的临床数据. 使用LLM生成的合成数据提高了检测准确性,即使删除了敏感信息.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 临床文本挖掘具有挑战性,因为医疗数据稀缺,敏感和不平衡.
- 从电子健康记录 (EHR) 中检测阿尔茨海默氏病 (AD) 的迹象和症状需要专门的专业知识.
研究的目的:
- 调查大型语言模型 (LLM) 在增强AD检测临床数据方面的潜力.
- 开发和评估用于AD标志和症状检测生成合成临床数据的方法.
主要方法:
- 为阿尔茨海默氏症的症状和症状进展创建了一个新的务实分类法.
- 三个数据集的生成:金色 (专家注释的电子健康记录),银色 (数据到标签方法) 和铜色 (标签到数据方法).
- 训练一个系统,使用生成的数据集来检测AD相关的迹象和症状.
主要成果:
- 与仅使用黄金数据集相比,使用银和铜数据集训练的系统显示出更好的性能.
- 通过结合专家知识,LLM可以生成用于复杂任务的合成临床数据.
- 标签到数据方法产生了高质量的,保护隐私的合成数据集.
结论:
- 在生成合成临床数据方面,LLM是有效的,以增强来自EHR的AD检测.
- 合成数据生成,特别是使用标签到数据方法,为临床文本挖掘中的数据稀缺性和隐私问题提供了可行的解决方案.
- 这种方法可以提高人工智能系统在识别复杂疾病方面的性能.
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